Longitudinal trajectories of spectral power during sleep in middle-aged and older adults

Age-related changes in sleep appear to contribute to cognitive aging and dementia. However, most of the current understanding of sleep across the lifespan is based on cross-sectional evidence. Using data from the Sleep Heart Health Study, we investigated longitudinal changes in sleep micro-architecture, focusing on whether such age-related changes are experienced uniformly across individuals. Participants were 2,202 adults (ageBaseline = 62.40 ± 10.38, 55.36 % female, 87.92 % White) who completed home polysomnography assessment at two study visits, which were 5.23 years apart (range: 4–7 years). We analyzed NREM and REM spectral power density for each 0.5 Hz frequency bin, including slow oscillation (0.5–1 Hz), delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), sigma (12–15 Hz), and beta-1 (15–20 Hz) bands. Longitudinal comparisons showed a 5-year decline in NREM delta (p <.001) and NREM sigma power density (p <.001) as well as a 5-year increase in theta power density during NREM (p =.001) and power density for all frequency bands during REM sleep (ps < 0.05). In contrast to the notion that sleep declines linearly with advancing age, longitudinal trajectories varied considerably across individuals. Within individuals, the 5-year changes in NREM and REM power density were strongly correlated (slow oscillation: r = 0.46; delta: r = 0.67; theta r = 0.78; alpha r = 0.66; sigma: r = 0.71; beta-1: r = 0.73; ps < 0.001). The convergence in the longitudinal trajectories of NREM and REM activity may reflect age-related neural de-differentiation and/or compensation processes. Future research should investigate the neurocognitive implications of longitudinal changes in sleep micro-architecture and test whether interventions for improving key sleep micro-architecture features (such as NREM delta and sigma activity) also benefit cognition over time.


Introduction
Since objective sleep assessment became available in the 20th century, researchers have investigated how, and why, sleep physiology changes during aging. [1][2][3] These basic science questions are important to translational, health issues. For example, because sleep is critical for cognition, age-related changes in sleep physiology may affect prefrontal cortex function, impede memory consolidation, or allow accumulation of metabolic products, each of which may contribute to cognitive decline and dementia. [4][5][6][7][8] Therefore, there is a need to understand how sleep typically changes with aging, including the rate of change at different ages, whether sleep changes consistently across individuals, and whether all aspects of sleep physiology change consistently within individuals (i.e., globally worsening versus some aspects of sleep declining and others showing preservation). [9].
Longitudinal studies have indicated a more complex picture of sleep changes with aging. For example, macroarchitecture analyses on participants in the longitudinal Sleep Heart Health Study (SHHS) revealed great interindividual variability in how sleep changed over time; some participants' macro-architecture worsened, some stayed the same, and some even improved. [9] Another noteworthy finding was that though cross-sectional analyses suggested a reduction in REM sleep with aging (consistent with prior cross-sectional research), the longitudinal data showed a 3-minute increase in REM sleep over 5 years. [9] This discrepancy raised the possibility that sleep and aging cross-sectional studies could be influenced by cohort effects or related biases; or, that NREM and REM sleep physiology change dynamically within individuals across time. In support of the latter view, longitudinal changes in SWS and REM sleep minutes were weakly associated, with patterns suggesting that an increase in REM sleep over time might compensate for a reduction in SWS in some individuals.
Dynamic changes in NREM and REM sleep with aging have received minimal attention. More often, researchers have investigated age effects on NREM and REM sleep independently, perhaps because the two sleep stages rely on different neural mechanisms. [30][31][32] As Carskadon and Dement put it, NREM and REM sleep have been considered ''as distinct from one another as each is from wakefulness." [33] Such conclusions have largely been based on studies with young animal models or healthy young adults. Recent theories of neurocognitive aging indicate that aging is accompanied by neural de-differentiation, during which neuronal mechanisms lose their specialization. [34] For example, the visual cues that are associated with activation in specific brain regions in young adults are associated with much wider-spread activation in older adults. [35] Furthermore, as aging brains lose their specialization, there is a corresponding decline in memory functioning. [36] These general neurobiological outcomes may have relevance to understanding how NREM and REM sleep interact during the aging process to influence cognitive outcomes (e.g., the sequential hypothesis of memory processing during sleep). [37][38][39][40].
In summary, current understanding of age-related changes in sleep physiology is mainly based on crosssectional evidence of relatively small convenience samples. Data from large, longitudinal studies will better inform how sleep physiology changes with aging. Moreover, it remains unclear whether the neural mechanisms that generate NREM and REM sleep also undergo age-related dedifferentiation. Therefore, the current study aims to inform these gaps in the literature by investigating longitudinal changes in NREM and REM sleep micro-architecture in a large sample of middle-to-older aged adults across 5 years. Detailed methods for the SHHS were previously published. [41] In summary, one-night home polysomnography was collected at each visit via EEG (C3-A2 and C4-A1 channels), electrooculography, chin electromyography, thoracic and abdominal bands, nasal-oral thermocouples, finger pulse oximetry, and bipolar electrocardiography. Spectral power density was calculated for each 0.5 Hz bin for 0.5 Hz-25 Hz and for slow oscillation (0.5-1 Hz), delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), sigma (12)(13)(14)(15), and beta-1 (15)(16)(17)(18)(19)(20), separated by REM and NREM sleep stages. Because only existing de-identified data were used in this study, approval from the university Institutional Review Board was waived.

Spectral
Prior to statistical analyses, power density values were normalized via log-transformation. [43] We additionally excluded outliers that were > 1.5 interquartile ranges from the 1st/3rd quartile. [44] Cross-sectionally, we conducted Pearson's correlations to test the association between age, macro-architecture, and micro-architecture of sleep at Visit 1. Longitudinally, we conducted paired-sample ttests to assess intra-individual changes in sleep and Pearson's correlations to determine the relationships between NREM and REM power density. We repeated the analyses in males (n = 983) and females (n = 1,219) separately and in middle-aged (age visit1 60; n = 984) and older participants separately (age visit1 > 60; n = 1218). Statistical analyses (two-tailed, alpha = 0.05) were performed using SPSS 28 and figures were generated using SAS 9.4 and GraphPad Prism 9.

Associations between macro-and micro-architecture
Participants' macro-and micro-architecture sleep characteristics at both visits are summarized in Table 1. Most correlations between sleep micro-architecture and macroarchitecture were small-sized ( Table 2). For example, greater NREM power density in the 0.5-15 Hz bands correlated with better sleep macro-architecture, including longer TST, shorter WASO, and higher SE (largest r = 0.15, p <.001). By contrast, greater NREM power density in beta-1 band correlated with shorter TST (r = -0.08, p <.001) and longer WASO (r = 0.04, p =.049). Moreover, greater REM delta and REM theta power density correlated with better sleep macro-architecture (e.g., longer TST and lower WASO) whereas greater REM power density in slow oscillation, sigma, and beta-1 bands correlated with poorer sleep (e.g., shorter TST, longer WASO, and lower SE; Table 2).

Table 3
Correlations between age and NREM and REM spectral power density.

Longitudinal correspondence between NREM and REM sleep features
We next examined the degree to which longitudinal changes in spectral power bands corresponded across NREM and REM stages. These stages have classically been conceptualized as qualitatively different brain states, with unique neural underpinnings. Interestingly though, Fig. 4 illustrates that longitudinal changes in NREM power density correlated strongly with changes  Fig. 4b shows that the NREM-REM correla-

Discussion
Longitudinal investigations of sleep changes with aging can produce different outcomes than cross-sectional approaches. While both cross-sectional and longitudinal analyses showed that NREM delta and sigma power decreased with aging, longitudinal analyses showed increases in NREM theta power and REM delta power. Moreover, whereas cross-sectional approaches can sometimes lead to simplified interpretations that sleep changes uniformly with advancing age, we observed evidence for non-linear changes in aging and substantial interindividual variability in the longitudinal trajectories of sleep micro-architecture. Some of this variability may be explained by gender differences in sleep microarchitecture, and some may be explained by neural dedifferentiation and compensatory mechanisms.
Our observations of a decline in NREM delta [20][21][22][23][24][25] and sigma power density [23,26] with aging are consistent with past studies. The decline in delta activity has previously been reported to be asymmetrical with the left centro-parietal region showing attenuated age-related decline, potentially explained by less gray matter atrophy in the dominant hemisphere (in right-handed participants). [24] Moreover, Carrier et al. (2011) found that aging is not only associated with lower slow-wave density, but also lower slow-wave amplitude and longer slow-wave positive and negative phase durations, suggesting lengthening of depolarization and hyperpolarization phases at the cellular level. [45] A decline in sigma power could be an indicator of a decrease in sleep spindles (i.e., reductions in spindle number, density, and duration). [18] These changes could be due to changes in the GABAergic mechanisms in the thalamocortical and intracortical circuitry, as well as reduced cortical volume. [18,46].
Our observation of non-linear changes for some measures (e.g., NREM alpha power) is a distinctive finding. Most cross-sectional studies of sleep and aging have implicated linear changes, [10] but a greater precision of trajectories with aging is afforded by longitudinal studies than in extreme-group (younger versus older) cross-sectional studies. One potential explanation is a non-linear degeneration of the thalamocortical circuitry that is responsible for generating alpha rhythms in the older adult group. [47] For example, attenuated alpha activity was observed during both wakefulness and NREM sleep in Alzheimer's disease patients, [48][49] and with advancing age there is increased incidence of undiagnosed cognitive impairment.
Another divergent finding was the observation of a longitudinal increase in NREM theta power, which was contrary to past cross-sectional findings. [21,[24][25][26][27] The divergent results could be explained by differences in study designs (cross-sectional [21,[24][25][26][27] vs longitudinal in SHHS), sleep environment (sleep laboratory [21,[24][25][26][27] vs at-home in SHHS), age range of the sample (up to 65 years old [21,[24][25][26][27] vs up to 90 years old in SHHS), and inclusion criteria (excluded [24][25][26][27] vs included indi-viduals with sleep apnea in SHHS). Additionally, patterns of age-related change in NREM theta power may depend on topography. [24,27] For example, Sprecher et al. (2016) observed that the age-related decline in NREM theta power was limited to frontal regions, and in the current study, only central regions were measured. [24] Beyond these methodological considerations, we observed lower NREM theta power to be associated with greater WASO and lower SE, though insomnia has been previously observed to be associated with increased NREM relative theta power. [50] Future research should clarify the associations between sleep macro-and micro-architecture to further our understanding of longitudinal changes in NREM theta activity.
The outcomes for REM sleep are also worth careful consideration. Cross-sectional studies suggested an agerelated decrease in lower frequency activity during REM, [27][28][29] whereas we observed longitudinal increases in REM power density in all frequency bands. The development of mild cognitive impairment and/or Alzheimer's disease in some individuals, which has been associated with slowing of REM spectral power, [49,[51][52] may drive the increases in lower frequency REM activity. Furthermore, the changes in REM sleep activity were primarily driven by females. While gender differences have been found cross-sectionally in sleep micro-architecture, [25,[53][54][55] the observation of gender differences in longitudinal trajectories of sleep micro-architecture is less documented. One potential explanation is that middle-aged females show thicker cortices in occipital, posterior cingulate, precentral, and postcentral regions than middle-aged males [56] and cortical thinning leads to a decline in NREM and REM delta activity. [57] It is also possible that the gender differences were not due to sleep physiology, but instead influenced by anatomical differences (e.g., skull thickness), [53] physiological differences (e.g., menopausal status), [58] or mental health differences (e.g., depression and anxiety). [59][60].
In addition to the influence of gender, we observed great inter-individual variability in how sleep microarchitecture changed over time. Such variability across people in longitudinal trajectories is contrary to the longstanding thinking of uniform and unidirectional changes in sleep, and may indicate age-related neural dedifferentiation [34] or compensatory mechanisms. [61] By the neural de-differentiation view, the high concordance in how NREM and REM EEG activity changes longitudinally is due to a de-segregation of neural mechanisms that were separately activated during NREM and REM sleep. By the compensation view, the strong correlations may indicate that additional neural systems are recruited in middle or older age to support hypothalamic or thalamocortical circuitry (and other typical generators of NREM and REM sleep) in compensation for the agerelated cellular loss and altered synaptic connectivity in these key regions. [61][62][63] In our study, compared to middle-aged adults, older adults showed an attenuation in the longitudinal concordance between NREM and REM activity in delta and theta bands, which may reflect compromised ability to compensate in the oldest age groups. Future studies could test the role of compensatory mecha-C. Gao and M.K. Scullin Aging Brain 3 (2023) 100058 nisms by linking neurocognitive outcomes with the degree of NREM and REM concordance. Another potential explanation for the differences between age groups in NREM-REM correspondence may be the neural mechanisms related to insomnia. For example, Wu et al. studied the cross-sectional correlations between EEG spectral power during eye-closed wakefulness and NREM sleep among good sleeper controls and primary insomnia patients. [64] Compared to the controls, the insomnia group showed weaker correlations in the delta, theta, and alpha bands, but a stronger correlation in the sigma band. Together with Wu et al.'s findings, our findings suggest that the mechanisms underlying delta and theta activity (e.g., regulation of homeostatic sleep drive [65] and sensory stimuli gating [66]) degenerate as a consequence of aging or primary insomnia.
The aging effect on sleep physiology has implications for age-related cognitive decline and dementia. [67] For example, EEG features during NREM and REM sleep are believed to reflect cortical integrity [57] and one's brain age. [68] Such structural changes in the brain may exert broad influences, including moderating the memory consolidation effects of sleep, [69] thereby causing the memory outcomes to be correlated differently with sleep across younger and older adults. [70][71] In some cases, cognitive processes may correlate with sleep physiology even in an opposite manner across younger and older brains. [72] According to the sequential hypothesis, NREM and REM collaboratively contribute to memory consolidation. [37][38] Therefore, studying neural dedifferentiation and compensation across NREM and REM sleep physiology will inform age-related cognitive decline and dementia.
Limitations of the current study include a lack of cognitive outcome data in the SHHS. The small proportion (12 %) of non-White participants also limits the investigation of racial/ethnic differences, or how sleep-memory trajectories may differ with aging in disadvantaged/marginalized groups. [73][74] In the SHHS, EEG data were only collected from the central channels and first-night effects could influence some outcomes (though such effects are often absent in home polysomnography studies). [76] Future studies with adaptation nights and more EEG channels will inform the EEG topography of the aging effects [24,27] and applying multitaper spectral analysis may illuminate agerelated changes in a manner that Fourier analyses do not. [17,75].
In conclusion, in this large, longitudinal sample, NREM power decreased in delta and sigma bands and that REM power increased in nearly all frequency bands with aging. There were sizeable individual differences in how EEG micro-architecture changed over 5 years, which may be influenced by gender differences, neural dedifferentiation, and/or compensatory mechanisms. Future studies should investigate whether the changes in spectral power features precipitate cognitive decline and predict neurodegenerative diseases (or vice versa). There is also a need for longitudinal randomized controlled trials to determine whether interventions can durably change sleep physiology with advancing age; and if so, whether such changes in sleep can durably benefit cognitive functioning.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.